1. Field of the Invention
The present invention relates to an image processing apparatus and a method of retrieving an image that is similar to an inputted image.
2. Description of the Related Art
In recent times, digital cameras and digital video camcorders have made it possible to easily capture still or moving pictures, and use the still picture or moving picture thus captured as image data. Documents have also been created by utilizing such image data.
A method of searching for image data that is suited for a given usage has consequently taken on increased significance. A method exists wherein classification or description information is initially attached to the image data, and using the information thus attached to retrieve and classify the image data. It is important to comprehend a content of the image in order to automatically attach classification or description information, however, which is very difficult to implement with a typical technology that performs recognition of the content of the image, and it is common to perform the attachment of such information manually at present.
A technology is coming into use that compares a graphical feature amount that is obtained from a color, brightness, or an edge of the image data, and retrieves an image that is similar in terms of the graphical feature amount thereof. The technology employs a variety of graphical feature amounts within the image, such as the color and a position thereof within the image, a constituent and an amount of items such as a line within the image, or a shape of an object within the image. In particular, when dealing with an image that is used within a text document, however, a circumstance occurs wherein such an action is performed upon the image as an enlargement, a reduction, a rotation, a cropping, or being used as a part of another image, depending upon a size or a layout of the image. It is consequently necessary to enable the retrieval of the image even when the image is used in such an altered manner.
In particular, the image data may be used for a variety of objectives, such as simplifying a creation operation by promoting a reuse of such a image data, or performing a censorship review of a content of a document from a security standpoint. As a consequence, a calculation of the feature amount, or a degree of similarity of the feature amount, of the image has been devised so as to enable a retrieval that is robust even when such an action is performed upon the image as the enlargement, the reduction, the rotation, the cropping, or being used as the part of another image.
Japanese Patent Laid-Open No. 8-279021, for instance, discloses a technology wherein correct image retrieval is performed even if a rotation is performed upon the image. The technology divides an input image into regions of a fixed size, and calculates the graphical feature amount within the regions thus divided. The feature amount is calculated from each respective feature amount by gathering each respective feature amount in a direction of an axis of rotation, such that the rotational robustness is present, and the retrieval that is performed uses the feature amount thus calculated. While the protocol includes a robustness with regard to the enlargement or the reduction of the image, it is only effective with regard to the rotational robustness when a center of the rotation is clear.
Japanese Patent Laid-Open No. 6-318256, for instance, uses a color of a portion within the image as the graphical feature amount, in order to maintain a robustness in the retrieval. A clustering and an indexing are performed with information of a location that is based on the feature amount and a computation of the feature amount. Attempting to perform the calculation that obtains a degree of similarity with a comparatively high precision with regard to calculating the degree of similarity of the feature amount of the image increases the cost of calculating the degree of similarity thereof, and thus, a technique for reducing the volume of calculation when calculating the degree of similarity when conducting a retrieval of the image data becomes important.
A reduction of the image or a number of the feature amount of the image that is targeted for the calculation of the degree of similarity is performed by refining such that only an index of the image that is targeted for retrieval is used, and excluding the image that is targeted for retrieval that does not have a comparatively high degree of similarity from being targeted for the calculation of the degree of similarity. Such an index, which is called a first order refinement indexing, is an effective technique for improving efficiency when carrying out the retrieval. In order to create such a first order refinement indexing, it is typical to perform a calculation of, for example, the clustering of the feature amount and the location information thereof, or of a self organizing map, and to group a result thereof. The calculation of such clustering of the feature amount and the location information thereof, or of the self organizing map, however, incurs a comparatively large calculation cost, causing an increase in the calculation load or time required to register the image such that the image will be available for retrieval.
In recent times, a method has been developed that treats, as a local feature amount of the image, a point or a portion of the image wherein for example a brightness within the image undergoes a significant change, as a feature point to be extracted, and uses the feature point thus extracted in a form such as a distance or a position relationship between the feature point within the image. Using the feature amount that is thus not present in the enlargement, the reduction, or the rotation of the image facilitates using the retrieval method that includes a degree of robustness against the enlargement, the reduction, or the rotation thereof.
It is typical to facilitate obtaining a plurality of the feature points with such a method, in order to increase the precision of the retrieval. As a consequence, either the calculation that is performed when comparing the feature amount of the plurality of the feature points increases in complexity, or else the volume of the calculation becomes very large. Accordingly, the use of the first order refinement index is also proposed, in order to reduce the load of the process of calculating the degree of similarity, as well as to implement retrieval at high speed.
When creating the first order refinement index, however, the quantity of the feature points of the image is large, and thus, the load of the process of clustering of the feature point grows to a significant level.